- author = "Larry M. Deschaine",
- title = "Simulation and Optimization of Large Scale Subsurface Environmental Impacts; Investigations, Remedial Design and Long Term Monitoring",
- journal = "Journal of Mathematical Machines and Systems",
- year = "2003",
- number = "3-4",
- pages = "201--218",
- address = "Kiev",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.immsp.kiev.ua/publications/eng/2003_3_4/index.html",
-
abstract = "The global impact to human health and the environment
from large scale chemical / radionuclide releases is
well documented. Examples are the wide spread release
of radionuclides from the Chernobyl nuclear reactors
and the mobilisation of arsenic in Bangladesh. The
seriousness of these issues is represented by the
activities of the World Health Organisation, the
Environmental Protection Agencies in Europe, the United
States, and the like. The fiscal costs of addressing
and remediating these issues on a global scale are
astronomical, but then so are the fiscal and human
health costs of ignoring them. An integrated complete
methodology for optimising the response(s) to these
issues is presented. This work addresses development of
global optimal response policy design for large scale,
complex, environmental issues. It is important to note
that optimization does not singularly refer to cost
minimisation, but to the effective and efficient
balance of cost, performance, risk, management, and
societal priorities along with uncertainty analysis.
This tool integrates all of these elements into a
single decision framework. It provides a consistent
approach to designing optimal solutions that are
tractable, traceable, and defensible.
Subsurface environmental processes are represented by linear and non-linear, elliptic and parabolic equations. The state equations for multi-phase flow (water, soil gas, NAPL), and multicomponent transport (radionuclides, heavy metals, volatile organics, explosives, etc.) are solved using numerical methods such as finite elements. Genetic programming is used to generate simulators from data when simulation models do not exist, to extend the accuracy of them, or to replace slow ones. To define and monitor the subsurface impacts, geostatistical numerical models, Kalman filtering and optimisation tools are integrated. Optimal plume finding is the estimation of the plume fringe(s) at a specified time using the least amount of sensors (i.e. monitoring wells). Long term monitoring extends this approach concept, and integrates the spatial-time correlations to optimise the decision variables of where to sample and when to sample over the project life cycle for least cost of achieving specified accuracy.
The remediation optimization solves the multi-component, multiphase system of equations and incorporates constraints on life-cycle costs, maximum annual costs, maximum allowable annual discharge (for assessing the monitored natural attenuation solution) and constraints on where remedial system component(s) can be located. It includes management overrides to force certain solutions be chosen or precluded from the solution design. It uses a suite of optimization techniques, including the outer approximation method, lipschitz global optimization, genetic algorithms, and the like. A discussion of using the WAVE-WP algorithm for distributed optimisation is included. This system process provides the full capability to optimise multi-source, multiphase, and multicomponent sites.
The results of applying just components of these algorithms have produced savings of as much as $90,000,000(US), when compared to alternative solutions. This was done without loss of effectiveness, and received an award from the Vice President of the United States.",
- notes = "UDC 681.3, Refs.: 45 titles. Extended Chalmers version 56 pages",
